Temporal action proposal generation aims to detect action instances with temporal boundaries and confidence in untrimmed videos.
Existing methods can be mainly divided into Top-down and Bottom-up methods.
The Top-down methods [1]}, [2]}, [3]}, [4]}, [5]} generate proposals using sliding windows or pre-defined anchors.
The Bottom-up methods mainly focus on evaluating “actionness”, which indicates the probability of a potential action, for each frame or clip in a video.
These works [6]}, [7]} use snippet-wise probability to generate candidate proposals.
BSN [8]} first proposes to predict start, end and actionness of each frame, then proposals are generated by constructing start and end points with high probabilities, with low confidence ones further abandoned by an evaluation module.
[9]}, [10]}, [11]}, [12]} generate all possible combinations of temporal locations to evaluate confidence of proposals.
[13]} generates coarse segment proposals by perceiving the whole video sequence and predicts the frame actionness by densely evaluating each video frame.
These methods evaluate action scores of proposals with rich clip-level context.
However, these methods fail to take full advantage of background by focusing only on the action score.
In our work, we predict the extra background score for the confidence of proposals to reduce low-quality proposals.
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